Predicting Parking Occupancy via Machine Learning in the Web of Things

Abstract The Web of Things (WoT) enables information gathered by sensors deployed in urban environments to be easily shared utilizing open Web standards and semantic technologies, creating easier integration with other Web-based information, towards advanced knowledge. Besides WoT, an essential aspect of understanding dynamic urban systems is artificial intelligence (AI). Via AI, data produced by WoT-enabled sensory observations can be analyzed and transformed into meaningful information, which describes and predicts current and future situations in time and space. This paper examines the impact of WoT and AI in smart cities, considering a real-world problem, the one of predicting parking availability. Traffic cameras are used as WoT sensors, together with weather forecasting Web services. Machine learning (ML) is employed for AI analysis, using predictive models based on neural networks and random forests. The performance of the ML models for the prediction of parking occupancy is better than the state of the art work in the problem under study, scoring an MSE of 7.18 at a time horizon of 60 minutes.